This paper proposes a novel sixth-generation (6G) multi-modal intelligent vehicle-to-vehicle (V2V) channel model from light detection and ranging (LiDAR) point clouds based on Synesthesia of Machines (SoM). To explore the mapping relationship between physical environment and electromagnetic space, we construct a new V2V high-fidelity mixed sensing-communication integration simulation dataset with different vehicular traffic densities (VTDs). Based on the constructed dataset, we develop a novel scatterer recognition (ScaR) algorithm utilizing neural network SegNet to recognize scatterer spatial attributes from LiDAR point clouds via SoM. Subsequently, the developed ScaR algorithm is incorporated into channel modeling to recognize scatterers in a physically informed manner. We further distinguish recognized scatterers into dynamic and static scatterers based on LiDAR point cloud features. The aforementioned procedures determine parameters related to dynamic and static scatterers, e.g., distance, angle, and number, enabling the channel model to generate the corresponding channel impulse responses (CIRs). Through the ScaR algorithm, dynamic and static scatterers change with the variation of LiDAR point clouds over time, which precisely models channel non-stationarity and consistency under different VTDs. Some important channel statistical properties, such as time-frequency correlation function (TF-CF) and Doppler power spectral density (DPSD), are obtained and analyzed. Simulation results match well with ray-tracing (RT)-based results, thus demonstrating the necessity of exploring the mapping relationship and the utility of the proposed model.

In this paper, a novel multi-modal intelligent channel model for sixth-generation (6G) multiple-uncrewed aerial vehicle (multi-UAV)-to-multi-vehicle communications is proposed. To thoroughly explore the mapping relationship between the physical environment and the electromagnetic space in the complex multi-UAV-to-multi-vehicle scenario, two new parameters, i.e., terrestrial traffic density (TTD) and aerial traffic density (ATD), are developed and a new sensing-communication intelligent integrated dataset is constructed in suburban scenario under different TTD and ATD conditions. With the aid of sensing data, i.e., light detection and ranging (LiDAR) point clouds, the parameters of static scatterers, terrestrial dynamic scatterers, and aerial dynamic scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different TTD and ATD conditions in the physical environment. In the proposed model, the channel non-stationarity and consistency on the time and space domains and the channel non-stationarity on the frequency domain are simultaneously mimicked. The channel statistical properties, such as time-space-frequency correlation function (TSF-CF), time stationary interval (TSI), and Doppler power spectral density (DPSD), are derived and simulated. Simulation results match ray-tracing (RT) results well, which verifies the accuracy of the proposed multi-UAV-to-multi-vehicle channel model.

In this paper, a novel adaptive near-field channel model with an extremely large-scale multiple-input multiple-output (XL-MIMO) uniform planar array (UPA) is proposed for sixth generation (6G) multiple-uncrewed aerial vehicle (multi-UAV) cooperative communications. In the proposed model, a novel selective near-field area (SNA) of the XL-MIMO UPA, where the transmission is regarded as spherical wavefront, is proposed to balance complexity and accuracy of near-field channel modeling. To jointly model the non-stationarity on the array, and in the space, time, and frequency domains, an adaptive UPA-UAV-time-frequency non-stationary algorithm is developed, which mimics the non-stationarity on the XL-MIMO UPA for the first time. The channel parameters related to the three-dimensional (3D) continuously arbitrary trajectory and self-rotation of multi-UAVs are also taken into account in the proposed model and the developed algorithm. To explore the channel statistics and validate the proposed model, a new XL-MIMO-UPA-to-multi-UAV channel dataset at low-terahertz (low-THz) frequency band under National Stadium scenario is built. Key UPA-to-multi-UAV channel statistics, such as the array-space-time-frequency correlation function (ASTF-CF), time stationary interval (TSI), Doppler power spectral density (DPSD), and singular value spread (SVS), are obtained. The close agreement between the simulation results and ray-tracing results in National Stadium scenario is achieved, demonstrating the accuracy of proposed channel model.

In this paper, a novel multi-modal uncrewed aerial vehicle (UAV)-to-ground channel model is proposed for sixth-generation (6G) intelligent sensing-communication integration, where communication information and sensing data, i.e., light detection and ranging (LiDAR) point cloud data, are integrated. To thoroughly explore the mapping relationship between the electromagnetic space and the physical environment in UAV-to-ground scenarios, a new intelligent sensing-communication integrated UAV-to-ground dataset with channel information and LiDAR point clouds is constructed in an urban scenario under low, medium, and high vehicular traffic density (VTD) conditions. By detecting dynamic and static objects in the physical environment with LiDAR point clouds, scatterers in the electromagnetic space are divided into dynamic and static scatterers. In addition, the parameters of dynamic and static scatterers in the electromagnetic space, e.g., number, distance, angle, and power, are quantified under different VTDs in the physical environment. In the proposed model, time non-stationarity and consistency along time axis and the frequency non-stationarity at different frequencies are simultaneously mimicked. The channel statistical properties, such as time-frequency correlation function and Doppler power spectral density (DPSD), are derived. Since simulation results match ray-tracing (RT) results well, the accuracy of the proposed multi-modal UAV-to-ground channel model in urban scenarios is validated.

In this paper, a novel channel modeling approach, named light detection and ranging (LiDAR)-aided geometry-based stochastic modeling (LA-GBSM), is developed. Based on the developed LA-GBSM approach, a new millimeter wave (mmWave) channel model for sixth-generation (6G) vehicular intelligent sensing-communication integration is proposed, which can support the design of intelligent transportation systems (ITSs). The proposed LA-GBSM is accurately parameterized under high, medium, and low vehicular traffic density (VTD) conditions via a sensing-communication simulation dataset with LiDAR point clouds and scatterer information for the first time. Specifically, by detecting dynamic vehicles and static buildings/trees through LiDAR point clouds via machine learning, scatterers are divided into static and dynamic scatterers. Furthermore, statistical distributions of parameters, e.g., distance, angle, number, and power, related to static and dynamic scatterers are quantified under high, medium, and low VTD conditions. To mimic channel non-stationarity and consistency, based on the quantified statistical distributions, a new visibility region (VR)-based algorithm in consideration of newly generated static/dynamic scatterers is developed. Key channel statistics are derived and simulated. By comparing simulation results and ray-tracing (RT)-based results, the utility of the proposed LA-GBSM is verified.

This paper proposes a novel three-dimensional (3D) non-stationary multiple-unmanned aerial vehicle (multi-UAV) cooperative channel model for sixth generation (6G) wireless communication systems. In the proposed multi-UAV cooperative channel model, the channel impulse response (CIR) of sub-channel between each UAV and ground station (GS) is calculated, including line-of-sight (LoS), non-LoS (NLoS), as well as ground reflection transmissions. The relative distance and transmission relationship among sub-channels between multi-UAVs and GS are further modeled and analyzed. To simultaneously capture the cooperative non-stationarity of multi-UAV cooperative channels in space and time domains, a novel cooperation-based space-time (S-T) non-stationary algorithm is developed based on the birth-death (BD) process for the first time. Meanwhile, the UAV-related parameters, e.g., the height and 3D movement of UAVs, are considered. Some important channel statistical properties, such as space-time-frequency correlation function (STF-CF), Doppler power spectral density (DPSD), cooperative time stationary interval, and singular value spread (SVS), are obtained. Based on the simulation results, multi-UAV cooperative channel statistical properties are analyzed. Finally, the simulation results match well with the ray-tracing results, which validates the accuracy of the proposed multi-UAV channel model.

In this paper, a mixed-bouncing based channel model with cooperative space-array-time (S-A-T) consistency and space-array-time-frequency (S-A-T-F) non-stationarity is proposed for sixth generation (6G) multiple-unmanned aerial vehicle (multi-UAV) cooperative communication systems with millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) technologies. To model the transmission propagation in multi-UAV integrated channels more accurately, the single-bouncing transmissions and multi-bouncing transmissions in multi-UAV integrated channels are simultaneously modeled and quantified by a cooperative cluster density index for the first time. Meanwhile, the transmissions through line-of-sight (LoS), ground reflection, single-bouncing, and multi-bouncing are captured. To jointly mimic cooperative S-A-T consistency and S-A-T-F non-stationarity in the integrated scattering environment (SE), a new cooperative consistent and non-stationary modeling algorithm is developed based on the frequency-dependent path gain, visibility region (VR), and birth-death (BD) survival probability. The channel parameters related to multi-UAVs are also taken into account in the developed algorithm. The corresponding multi-UAV cooperative channel statistical properties are derived by taking mixed-bouncing transmission into account. Meanwhile, the accuracy of the mixed-bouncing based multi-UAV integrated channel model with cooperative S-A-T consistency and S-A-T-F non-stationarity is validated as simulation results match well with ray-tracing results.

In this paper, a novel three-dimensional (3D) irregular shaped geometry-based stochastic model (IS-GBSM) is proposed for sixth-generation (6G) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) channels. To investigate the impact of vehicular traffic density (VTD) on channel statistics, clusters are divided into static clusters and dynamic clusters, which are further distinguished into static/dynamic single/twin-clusters to capture the mixed-bouncing propagation. A new method, which integrates the visibility region and birth-death process methods, is developed to model space-time-frequency (S-T-F) non-stationarity of V2V channels with time-space (T-S) consistency. The continuously arbitrary vehicular movement trajectory (VMT) and soft cluster power handover are modeled to further ensure channel T-S consistency. From the proposed model, key channel statistics are derived. Simulation results show that S-T-F non-stationarity of channels with T-S consistency is modeled and the impacts of VTD and VMT on channel statistics are analyzed. The generality of the proposed model is validated by comparing simulation results and measurement/ray-tracing (RT)-based results.

In this paper, a novel unmanned aerial vehicle (UAV) space-time-frequency (S-T-F) non-stationary channel model with time-space consistency for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems is proposed. In the proposed model, the line-of-sight (LoS) transmission and non-LoS (NLoS) transmission through ground reflection, single-clusters, and twin-clusters are modeled. Meanwhile, the three-dimensional (3D) continuously arbitrary trajectory and the self-rotation of UAV are imitated. To capture the time-space consistency and S-T-F non-stationarity simultaneously, a new UAV-related non-stationary modeling algorithm that integrates the visibility region (VR), frequency-dependent path gain, and survival probability is developed for the first time. In this algorithm, the UAV-related parameters are considered, including UAV’s height, 3D moving velocity, and self-rotation angles. In the proposed model, the calculation of channel impulse response (CIR) is developed, which considers the cluster density index influenced by communication scenarios, frequency, UAV’s height, and the distance between transceivers. Some important channel statistical properties, such as S-T-F correlation function (STF-CF), Doppler power spectral density (DPSD), and stationary interval, are derived. Finally, simulation results match well with ray-tracing-based results, which verifies the utility of the proposed model.

In this paper, a novel cooperative array-space-time-frequency (A-S-T-F) non-stationary multi-unmanned aerial vehicle (UAV) channel model with three-dimensional (3D) continuously arbitrary trajectory and self-rotation for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems is proposed. The channel impulse response (CIR) of the proposed multi-UAV cooperative channel model is derived, including line-of-sight (LoS), ground reflection, and non-LoS (NLoS) transmissions. To capture the 3D continuously arbitrary trajectories of multi-UAVs, the 3D time-varying accelerations of multi-UAVs, clusters, and ground station (GS) and the multi-UAVs’ self-rotations are taken into account. In addition, to model the non-stationarity of massive MIMO mmWave multi-UAV cooperative channels in the A-S-T-F domain, a new cooperative A-S-T-F non-stationary algorithm based on the birth-death (BD) process and the$K$-Means clustering algorithm is developed. Important cooperative channel statistical properties, including cooperative array-space-time-frequency correlation function (ASTF-CF), cooperative Doppler power spectral density (DPSD), cooperative time stationary interval, and singular value spread (SVS), are derived and investigated. The accuracy of the proposed multi-UAV cooperative channel model is verified by the close agreement between ray-tracing-based results and simulation results.

This paper proposes a novel three-dimensional (3D) mixed-bouncing based unmanned aerial vehicle (UAV) channel model with space-time-frequency (S-T-F) non-stationarity for sixth generation (6G) massive multiple-input-multiple-output (MIMO) millimeter wave (mmWave) wireless communication systems. In the proposed mixed-bouncing based model, the line-of-sight (LoS) transmission, single-bouncing transmission, and multi-bouncing transmission are simultaneously modeled. A new parameter, i.e., cluster density index, is defined and developed. The influence of communication scenarios, frequency, UAV’s height, and distance between transceivers on cluster density index is also analyzed. To capture the S-T-F non-stationarity, a mixed-bouncing based S-T-F non-stationary algorithm is developed based on the birth-death (BD) process and frequency-dependent factor for the first time. In the developed algorithm, the non-stationarity is captured from the perspectives of both single-clusters and twin-clusters. Meanwhile, the impact of UAV-related parameters, such as the UAV’s height and 3D moving velocity, on the non-stationary modeling is considered. Based on the simulation results, the impact of cluster density index on the important channel statistical properties, such as S-T-F correlation function (STF-CF) and Doppler power spectral density (PSD), is explored. Finally, the simulation results match well with the measurement and ray-tracing (RT)-based results, validating the accuracy of the proposed model.

This paper proposes a novel general sixth-generation (6G) irregular-shaped geometry-based stochastic model (IS-GBSM) for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. The proposed IS-GBSM can model spherical wavefront propagation, high delay resolution, and three-dimensional (3D) space-time-frequency non-stationarity of channels with time-space consistency, and thus can be applied to integrated communication and sensing systems. To capture the 3D non-stationarity with channel consistency, a new hybrid method combining geometric and parametric methods is developed. Based on the geometric method, i.e., visibility region (VR), the smooth and consistent appearance and disappearance of clusters during array-time evolution are modeled. With the help of the parametric method, a frequency-dependent path gain is introduced, and a visible factor based on uniform continuity theorem and bounded variation function is further proposed to capture the soft cluster power handover during cluster array-time evolution. Key channel statistics of the proposed IS-GBSM are derived and investigated. Simulation results demonstrate that the space-time-frequency non-stationarity is modeled and time-space consistency is captured. Finally, the utility of the proposed IS-GBSM is verified by the excellent agreement between simulation results and measurement.

This paper proposes a novel three-dimensional (3D) non-stationary irregular-shaped geometry-based stochastic model (IS-GBSM) for beyond fifth-generation (B5G) and sixth-generation (6G) vehicle-to-vehicle (V2V) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) channels. By distinguishing dynamic clusters and static clusters, the proposed IS-GBSM for the first time explores the impact of vehicular traffic density (VTD) on channel statistics in B5G/6G V2V mmWave massive MIMO scenarios. Furthermore, a novel method is developed to model the channel space-time-frequency non-stationarity. In the developed method, dynamic/static correlated clusters are first generated by an improved$K$-Means clustering algorithm. Then, by employing a birth-death process based on correlated clusters, the consistency in birth and death between dynamic/static correlated clusters during time-array evolution is modeled. Key channel statistical properties, e.g., space-time-frequency correlation function (STF-CF) and Doppler power spectral density (DPSD), are derived. Simulation results demonstrate that the space-time-frequency non-stationarity is captured and the influence of VTDs on channel statistics is explored. Finally, the close agreement is achieved between simulation results and measurements, verifying the utility of proposed IS-GBSM.

In this paper, based on the geometric stochastic modeling method, a space-time-frequency (S-T-F) non-stationary model with three-dimensional (3D) continuously arbitrary trajectory and self-rotation is proposed for sixth generation (6G) massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) unmanned aerial vehicle (UAV) channels. It is the first 6G massive MIMO mmWave UAV channel model that considers the 3D continuously arbitrary trajectory of UAV in practice and models S-T-F non-stationarity of 6G UAV channels. In the proposed model, the calculation of channel impulse response (CIR) is developed, which considers the 3D time-varying accelerations and self-rotations of transceivers and clusters. To further model the S-T-F non-stationarity of UAV channels, a novel UAV-related birth-death (BD) algorithm based on correlated clusters is developed. In the developed algorithm, the impact of typical UAV-related parameters, e.g., the UAV’s moving direction, altitude, and time-varying velocity, on the setting of correlated clusters and the BD process is sufficiently considered. Important channel statistical properties are derived and investigated. Some numerical results and interesting observations are given, which can provide some assistance for the design of 6G massive MIMO mmWave UAV communication systems. Finally, the utility of the proposed model is verified by the close agreement between simulation results and ray-tracing-based results.

This paper proposes a non-stationary three-dimensional (3D) irregular-shaped geometry-based stochastic model (IS-GBSM) for fifth generation (5G) and beyond massive multiple-input multiple-output (MIMO) millimeter wave (mmWave) unmanned aerial vehicle (UAV) channels. This is the first sixth generation (6G) massive MIMO mmWave UAV IS-GBSM that can model the UAV channel space-time non-stationarity, and can describe the impact of some unique UAV-related parameters, e.g., the UAV’s moving direction, height, and speed, on channel statistical properties. To better represent the space-time non-stationarity in UAV scenarios, a novel UAV-related space-time cluster evolution algorithm is developed. The developed algorithm considers the characteristics of UAV communications on the modeling of space-time non-stationarity. Based on the proposed model, some channel statistical properties are derived and thoroughly investigated, including the space-time-frequency correlation function, Doppler power spectral density, envelope level crossing rate, and average fade duration. Some numerical results and interesting observations are given, and the impact of UAV-related parameters on channel statistical properties is explored, which can provide assistance for the design of 6G massive MIMO mmWave UAV communication systems. Finally, the applicability of the proposed model is verified by the close agreement between simulation results and measurement.

This article proposes a novel 3-D nonstationary wideband vehicle-to-vehicle (V2V) geometry-based stochastic model (GBSM) with uniform planar antenna arrays (UPAs) for massive multiple-input–multiple-output (MIMO) wireless communication systems. In the proposed GBSM, a novel method, so-called birth–death (BD) process and seed algorithm-based selective cluster evolution, is developed to capture the space nonstationarity of V2V massive MIMO with UPA channels. The time nonstationarity is further mimicked by employing this novel method over the entire timeline. In addition, the proposed GBSM not only models the reflection of the ground, but also divides clusters into static clusters and dynamic clusters to sufficiently investigate the impact of vehicular traffic density (VTD) on channel statics. The channel parameters are properly calculated by 3-D vectors, resulting in the proposed GBSM with high accuracy and low complexity. Important statistical properties, such as the space-time correlation function (S-T CF), spatial cross-correlation function (CCF), temporal auto-correlation function (ACF), and Doppler power spectrum density (PSD) are derived and thoroughly investigated. Simulation results show that the space-time nonstationarity is successfully mimicked and the VTD has a significant impact on channel statistics. Finally, an excellent agreement is achieved between simulation results and measurements, validating the accuracy of the proposed GBSM.

In this paper, a general three-dimensional (3D) space-time-frequency non-stationary model is proposed for sixth generation (6G) channels. From the proposed model, a novel method, so-called correlated cluster based birth-death (BD) process method, is developed to efficiently and jointly mimic the 3D channel space-time-frequency non-stationarity. In this developed method, the frequency non-stationarity is properly captured by correlated clusters, which are obtained via an unsupervised learning algorithm in machine learning, i.e., K-Means clustering algorithm. Additionally, the developed method involves the cluster based space-time non-stationary modeling. Based on the correlation coefficient of clusters, the BD probabilities on the array and time axes are reasonably modified by the linear weight method and matrix iteration algorithm. Therefore, interactions among the space, time, and frequency non-stationary modeling are sufficiently considered. Important channel statistical properties are derived and thoroughly investigated. Simulation results demonstrate that the channel non-stationarity in space-time-frequency domains can be sufficiently characterized. Finally, the excellent agreement between the simulation results and measurements further verifies the accuracy of the proposed model.

As vehicular communications for beyond fifth‐generation (B5G) and sixth‐generation (6G) is picking up interests from academia and industry recently, more and more research and development have been devoted towards the establishment of vehicular communications for B5G and 6G that is capable of supporting the ever more intelligent transportation systems. One key facilitating the design and improvement of vehicular communications for B5G and 6G is channel modelling, which is widely regarded as the foundation of all communication and networking systems. In this paper, the authors focus on the research and analysis of B5G and 6G vehicular channel measurements and modelling. By emphasising the new requirements and challenges that the emerging B5G and 6G technologies and frequency bands bring to vehicular communication channel measurements and modelling, they present an overview of the existing work and identify the limitations therein, and provide guidelines on the channel model development and adoption for various system development and verification objectives. Finally, future challenges related to vehicular channel measurements, modelling, and their application for B5G and 6G are addressed.

In this study, the authors propose a new non‐geometrical stochastic model (NGSM) for non‐stationary wideband vehicular communication channels. To include the line‐of‐sight component, the proposed model first generates a non‐uniformly distributed tap phase, which can be obtained from the widely used uniformly distributed tap phase. Moreover, the proposed model can practically experience variable types of Doppler spectra for different delays by modifying the autocorrelation function used in the existing NGSM. In consideration of the non‐stationarity in the frequency domain of vehicular communication channels, the authors further consider that the amplitude and phase of different taps are correlated. To evaluate the performance of the proposed model, they derive some significant statistical properties in terms of the power delay profile, tap correlation coefficient matrix and Doppler power spectral density. It is demonstrated that in comparison with the existing NGSMs, the proposed model possesses the ability to better mimic characteristics of real vehicular channels. Finally, the excellent agreement is achieved between the simulation results and the corresponding measured data, confirming the accuracy of the proposed model.